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Zero
| # -*- coding: utf-8 -*- | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| import copy | |
| import logging | |
| from typing import Any, Dict, List, Tuple | |
| import torch | |
| from detectron2.data import MetadataCatalog | |
| from detectron2.data import detection_utils as utils | |
| from detectron2.data import transforms as T | |
| from detectron2.layers import ROIAlign | |
| from detectron2.structures import BoxMode | |
| from detectron2.utils.file_io import PathManager | |
| from densepose.structures import DensePoseDataRelative, DensePoseList, DensePoseTransformData | |
| def build_augmentation(cfg, is_train): | |
| logger = logging.getLogger(__name__) | |
| result = utils.build_augmentation(cfg, is_train) | |
| if is_train: | |
| random_rotation = T.RandomRotation( | |
| cfg.INPUT.ROTATION_ANGLES, expand=False, sample_style="choice" | |
| ) | |
| result.append(random_rotation) | |
| logger.info("DensePose-specific augmentation used in training: " + str(random_rotation)) | |
| return result | |
| class DatasetMapper: | |
| """ | |
| A customized version of `detectron2.data.DatasetMapper` | |
| """ | |
| def __init__(self, cfg, is_train=True): | |
| self.augmentation = build_augmentation(cfg, is_train) | |
| # fmt: off | |
| self.img_format = cfg.INPUT.FORMAT | |
| self.mask_on = ( | |
| cfg.MODEL.MASK_ON or ( | |
| cfg.MODEL.DENSEPOSE_ON | |
| and cfg.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS) | |
| ) | |
| self.keypoint_on = cfg.MODEL.KEYPOINT_ON | |
| self.densepose_on = cfg.MODEL.DENSEPOSE_ON | |
| assert not cfg.MODEL.LOAD_PROPOSALS, "not supported yet" | |
| # fmt: on | |
| if self.keypoint_on and is_train: | |
| # Flip only makes sense in training | |
| self.keypoint_hflip_indices = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN) | |
| else: | |
| self.keypoint_hflip_indices = None | |
| if self.densepose_on: | |
| densepose_transform_srcs = [ | |
| MetadataCatalog.get(ds).densepose_transform_src | |
| for ds in cfg.DATASETS.TRAIN + cfg.DATASETS.TEST | |
| ] | |
| assert len(densepose_transform_srcs) > 0 | |
| # TODO: check that DensePose transformation data is the same for | |
| # all the datasets. Otherwise one would have to pass DB ID with | |
| # each entry to select proper transformation data. For now, since | |
| # all DensePose annotated data uses the same data semantics, we | |
| # omit this check. | |
| densepose_transform_data_fpath = PathManager.get_local_path(densepose_transform_srcs[0]) | |
| self.densepose_transform_data = DensePoseTransformData.load( | |
| densepose_transform_data_fpath | |
| ) | |
| self.is_train = is_train | |
| def __call__(self, dataset_dict): | |
| """ | |
| Args: | |
| dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format. | |
| Returns: | |
| dict: a format that builtin models in detectron2 accept | |
| """ | |
| dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below | |
| image = utils.read_image(dataset_dict["file_name"], format=self.img_format) | |
| utils.check_image_size(dataset_dict, image) | |
| image, transforms = T.apply_transform_gens(self.augmentation, image) | |
| image_shape = image.shape[:2] # h, w | |
| dataset_dict["image"] = torch.as_tensor(image.transpose(2, 0, 1).astype("float32")) | |
| if not self.is_train: | |
| dataset_dict.pop("annotations", None) | |
| return dataset_dict | |
| for anno in dataset_dict["annotations"]: | |
| if not self.mask_on: | |
| anno.pop("segmentation", None) | |
| if not self.keypoint_on: | |
| anno.pop("keypoints", None) | |
| # USER: Implement additional transformations if you have other types of data | |
| # USER: Don't call transpose_densepose if you don't need | |
| annos = [ | |
| self._transform_densepose( | |
| utils.transform_instance_annotations( | |
| obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices | |
| ), | |
| transforms, | |
| ) | |
| for obj in dataset_dict.pop("annotations") | |
| if obj.get("iscrowd", 0) == 0 | |
| ] | |
| if self.mask_on: | |
| self._add_densepose_masks_as_segmentation(annos, image_shape) | |
| instances = utils.annotations_to_instances(annos, image_shape, mask_format="bitmask") | |
| densepose_annotations = [obj.get("densepose") for obj in annos] | |
| if densepose_annotations and not all(v is None for v in densepose_annotations): | |
| instances.gt_densepose = DensePoseList( | |
| densepose_annotations, instances.gt_boxes, image_shape | |
| ) | |
| dataset_dict["instances"] = instances[instances.gt_boxes.nonempty()] | |
| return dataset_dict | |
| def _transform_densepose(self, annotation, transforms): | |
| if not self.densepose_on: | |
| return annotation | |
| # Handle densepose annotations | |
| is_valid, reason_not_valid = DensePoseDataRelative.validate_annotation(annotation) | |
| if is_valid: | |
| densepose_data = DensePoseDataRelative(annotation, cleanup=True) | |
| densepose_data.apply_transform(transforms, self.densepose_transform_data) | |
| annotation["densepose"] = densepose_data | |
| else: | |
| # logger = logging.getLogger(__name__) | |
| # logger.debug("Could not load DensePose annotation: {}".format(reason_not_valid)) | |
| DensePoseDataRelative.cleanup_annotation(annotation) | |
| # NOTE: annotations for certain instances may be unavailable. | |
| # 'None' is accepted by the DensePostList data structure. | |
| annotation["densepose"] = None | |
| return annotation | |
| def _add_densepose_masks_as_segmentation( | |
| self, annotations: List[Dict[str, Any]], image_shape_hw: Tuple[int, int] | |
| ): | |
| for obj in annotations: | |
| if ("densepose" not in obj) or ("segmentation" in obj): | |
| continue | |
| # DP segmentation: torch.Tensor [S, S] of float32, S=256 | |
| segm_dp = torch.zeros_like(obj["densepose"].segm) | |
| segm_dp[obj["densepose"].segm > 0] = 1 | |
| segm_h, segm_w = segm_dp.shape | |
| bbox_segm_dp = torch.tensor((0, 0, segm_h - 1, segm_w - 1), dtype=torch.float32) | |
| # image bbox | |
| x0, y0, x1, y1 = ( | |
| v.item() for v in BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) | |
| ) | |
| segm_aligned = ( | |
| ROIAlign((y1 - y0, x1 - x0), 1.0, 0, aligned=True) | |
| .forward(segm_dp.view(1, 1, *segm_dp.shape), bbox_segm_dp) | |
| .squeeze() | |
| ) | |
| image_mask = torch.zeros(*image_shape_hw, dtype=torch.float32) | |
| image_mask[y0:y1, x0:x1] = segm_aligned | |
| # segmentation for BitMask: np.array [H, W] of bool | |
| obj["segmentation"] = image_mask >= 0.5 | |